1. Field of the Invention
The present invention relates to an image recognition technique.
2. Description of the Related Art
As one recognition technique, extensive studies have been made to cause a computer to learn a feature amount extracted from a target object image obtained by an image capturing unit and recognize the type of object in an input image. It has also been studied to estimate not only the type but also the position and orientation using object model information or the like. An application example of this technique is position/orientation recognition of parts to perform work such as advanced assembly by a robot.
Non-patent literature 1 (B. Leibe, “Robust Object Detection with. Interleaved Categorization and Segmentation”, IJCV Special Issue on Learning for Vision for learning, August 2007) proposes a method of making a feature in a code book obtained from learned images correspond to a detected feature, and estimating the center position of an object by probabilistic voting (implicit-shape-model). This method can estimate not only the type but also the object position.
In patent literature 1 (Japanese Patent Laid-Open No. 2008-257649), a feature point is extracted from an input image, and its feature amount is calculated. A feature point having almost the same feature amount as that of a feature point in a learned image is set as a corresponding point. The reference point is voted for each corresponding point in the input image based on the feature amount (including position information) of a feature point in the learned image, thereby recognizing a target object and estimating its position.
However, the object recognition technique using an image takes a long processing time because a feature is extracted from an image and made to correspond to a feature obtained from a learned image. Further, not all features are useful for recognizing the target object.
In patent literature 2 (Japanese Patent Laid-Open No. 2009-37640), a partial region used for learning is sequentially changed in pattern recognition (character recognition). Every result obtained by recognizing a learning pattern is evaluated, selecting a plurality of partial regions used for learning.
For some recognition target objects, a portion useful for identifying the type, position, and orientation of a target object is known in advance. For example, when the type, position, and orientation of a part are to be recognized in automatic assembly by a robot and part of a rotationally symmetrical member has a notch, the orientation can be determined uniquely by recognizing the notch. However, it is generally difficult to efficiently learn and recognize the notch of the part.
However, patent literature 1 does not describe a method for defining feature points used for learning. When the method in patent literature 2 is applied, selection of a partial region takes a very long time because a learning pattern is recognized and evaluated every time a partial region is added.